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Ahmed E. Hassan

5 papers here

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author1
  • last author4

Across the 5 of 5 papers where every author was matched, so the position is known.

fields
  • cs.SE4
  • cs.DS1
ORCID 0000-0001-7749-5513
same name
  • Ahmed E. Hassan — 23 papers, h 7
  • Ahmed E. Hassan — 18 papers, h 7
  • Ahmed E. Hassan — 9 papers, h 22
  • Ahmed E. Hassan — 9 papers, h 2
  • Ahmed E. Hassan — 6 papers, h 4
  • Ahmed E. Hassan — 5 papers, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20222025
most citedAn empirical study of testing machine learning in the wild

1 citations · 1 across the 5 of their papers we have counts for

collaborators
Showing cs.SEShow all

4 papers · 1 filter

cs.SE2025

MobileUPReg: Identifying User-Perceived Performance Regressions in Mobile OS Versions

Wei Liu, Yi Wen Heng, Feng Lin +3

Mobile operating systems (OS) are frequently updated, but such updates can unintentionally degrade user experience by introducing performance regressions. Existing detection techni…

cs.SE2025

Screencast-Based Analysis of User-Perceived GUI Responsiveness

Wei Liu, Linqiang Guo, Yi Wen Heng +4

GUI responsiveness is critical for a positive user experience in mobile applications. Even brief delays in visual feedback can frustrate users and lead to negative reviews. However…

cs.SE2023★ 1 cited

An empirical study of testing machine learning in the wild

Moses Openja, Foutse Khomh, Armstrong Foundjem +4

Recently, machine and deep learning (ML/DL) algorithms have been increasingly adopted in many software systems. Due to their inductive nature, ensuring the quality of these systems…

cs.SE2023

On the Model Update Strategies for Supervised Learning in AIOps Solutions

Yingzhe Lyu, Heng Li, Zhen Ming +2

AIOps (Artificial Intelligence for IT Operations) solutions leverage the massive data produced during the operation of large-scale systems and machine learning models to assist sof…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.